Although flexible processes are deemed critical for many companies and constitute a key concern of business process management, there is a lack of approaches for valuating process flexibility from an economic perspective and for determining an appropriate level of process flexibility. Today, companies do not know how flexible their processes should be. While generally advocating balanced investments, scholars provide concrete recommendations for very specific settings only. What is missing is a more general guidance and a deeper investigation of the positive economic effects of flexible processes, which are hard-to-measure and beset with risks. Against this backdrop, we propose an optimization model that enables determining the optimal level of process flexibility in line with the principles of value-based business process management. We also report on the insights gained from applying the optimization model to the production processes of an international company from the semi-conductor industry.

1. University of Augsburg
Patrick Afflerbach, Gregor Kastner,
Felix Krause, Maximilian Röglinger
Research Center
Finance & Information Management
An optimization model for
valuating process flexibility
Fraunhofer Project Group
Business & Information Systems Engineering
Department of Information Systems Engineering
& Financial Management
Elite Graduate Program
Finance & Information Management
www.fim-rc.de/en
Presentation at the International Conference
on Information Systems (ICIS), Milan
II/2010-101007
December 17th 2013
www.fit.fraunhofer.de/bise

2. Motivation
Challenges derived from the literature
Challenge 1: Provide more
general guidance for decisions
on process flexibility!
Challenge 2: Focus on
positive economic effects of
process flexibility!
Challenge 3: Explore the
benefits of treating flexibility
as a multi-process concept!
2 • M. Röglinger and P. Afflerbach • An optimization model for valuating process flexibility
© FIM Research Center

3. Theoretical background
Defining process flexibility
Volume flexibility enables
to increase or decrease
production above or below
the installed capacity.
Functional flexibility refers to
the readiness with which tasks
can be changed in response to
varying business demands.
(Goyal and Netessine 2011)
A
B
A
C
B‘
D
(Sethi and Sethi 1990)
Process flexibility is the ability to
create multiple outputs on the same
capacity, and to reallocate capacity
between processes in response to
realized demand.
(inspired by Goyal and Netessine 2011)
3 • M. Röglinger and P. Afflerbach • An optimization model for valuating process flexibility
© FIM Research Center

4. Optimization model
General setting
General setting:
A
B
C
Providing process:
 Inferior output
 Lower profit margin
 Provides flexible capacity
 Implements flexibility projects
A
B‘
D
Receiving process:
 Superior output
 Higher profit margin
 Receives flexible capacity
Assumptions and definitions:
 Process flexibility is the percentage of the
capacity of the providing process that can be
reallocated to create the superior output.
 The demand for both process outputs is risky.
It is uniformly distributed and scatters symmetrically
around the respective capacity.
 Selling the superior output is such profitable
that capacity is always reallocated if needed.
 Decision makers aim to identify the level of
flexibility potential that maximizes the risk-adjusted
expected present value of the process cash flow.
4 • M. Röglinger and P. Afflerbach • An optimization model for valuating process flexibility
© FIM Research Center

5. Optimization model
Basic idea – An example case
Providing process:
Flexibility
potential
Receiving process:
Flexibility
potential
Demand
Minimum
Demand
Remaining Capacity
capacity
Demand
Maximum
Demand
Minimum
Demand
Provided
flexibility
Capacity
Maximum
Demand
Needed
flexibility
Characteristics:
Cash flow effects:
 Investment phase: Flexibility potential is
established by implementing flexibility projects.
 Investment phase: Cash outflows for
implementing flexibility projects.
 Operations phase: Flexible capacity is used to
create superior output in response to the realized
demand.
 Operations phase (per period): (a) Increased
cash inflows from the superior output, (b) no
decreased cash inflows from the inferior output.
5 • M. Röglinger and P. Afflerbach • An optimization model for valuating process flexibility
© FIM Research Center

6. Optimization model
Cash inflows: Case analysis to cope with risky demand
(Periodic) Cash inflows
Providing process
Receiving process
Probability = 0.5
Probability = 0.5
Demand
Demand
Case 1: No increased
cash inflows from
superior output.
Case 2: Increased
cash inflows from
superior output.
Probability = 0.5
Probability = 0.5
Demand
Case 2.1: Reduced cash
inflows from inferior
output are certain.
Demand
Case 2.2: Reduced cash
inflows from inferior output
are possible.
6 • M. Röglinger and P. Afflerbach • An optimization model for valuating process flexibility
© FIM Research Center

7. Optimization model
Cash outflows: Important drivers
Idea:
Cash
outflows
1. Start with the full replication of the
receiving process as a worst-case
scenario.
2. Use process characteristics to reduce
the worst-case cash outflows (e.g.,
criticality, similarity, variability).
Flexibility
potential
Worst-case
cash
outflows
3. Adjust cash outflows to the desired
level of flexibility potential considering
an overhead factor for administration
and coordination.
Process
characteristics
Critical
steps
Overhead
factor
Similarity
of critical
steps
Example:
Providing process:
A
B

B‘
A
Receiving process:
C

D
A
B‘
D
Step D is uncritical because it does
not cause a capacity shortage.
7 • M. Röglinger and P. Afflerbach • An optimization model for valuating process flexibility
© FIM Research Center

8. Optimization model
Integrating cash inflows and cash outflows
Properties of the cash inflows:
 Present value based on risk-adjusted
interest rate
 Strictly monotonically increasing
 Strictly concave
Properties of the cash outflows:
 Strictly monotonically increasing
 Strictly convex
0,0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1,0
𝐹
𝐹∗
Properties of the cash flow:
 Difference of inflows and outflows
Cash outflows
 Strictly concave
Risk-adjusted expected present-value cash inflows
 Maximum as single extreme point
Risk-adjusted expected present-value cash flow
 Maximum can be determined analytically
8 • M. Röglinger and P. Afflerbach • An optimization model for valuating process flexibility
© FIM Research Center

9. Real-world application in the semi-conductor industry
Providing process:
Metal Layer
production
2x
Receiving process:
Photo
Layer
production
12 x
Metal Layer
production
3x
Case context:
 Investment case from one of the company’s
semi-conductor factories in South Asia
 Industry: high output variety, short
lifecycles, highly fluctuating demand, huge
investments
 Contact: Management of the strategic
production planning department
Approach:
 First interview: Discussion of the model
and adaptation of distinct model components
 Second interview: Data collection and
application of the model
Photo
Layer
production
24 x
Special Photo
Layer
production
3x
Providing process
Receiving process
Output
super junction metaloxide-semiconductor
field-effect transistor
(SMOSFET)
SMOSFET + bipolar
junction transistor
(SM-BJT)
Profit
margin
326 EUR
per wafer
896 EUR
per wafer
Expected
demand
1,000 wafer
per week
200 wafer
per week
Demand
deviation
336 wafer
per week
200 wafer
per week
Capacity
1,000 wafer
per week
0 wafer
per week
9 • M. Röglinger and P. Afflerbach • An optimization model for valuating process flexibility
© FIM Research Center

10. Real-world application in the semi-conductor industry
Question: Was the company’s last investment in a flexible photolithography
machine as well as the corresponding training measures and IT support reasonable?
Further input and calculations:
Cash flow effects:
Value
Cash outflows
Enabling effect
3 Mio. EUR
+200 SM-BJT
(-300 SMOSFET)
wafer per week
Value
Expected cash inflows per week
Expected present-value cash inflows
29,000 EUR
16.2 Mio. EUR
Insights:
Flexibility potential
30 %
 Investment in process flexibility was reasonable!
Exchange rate
0.67
 An investment of about 2.4 Mio. EUR would have
been optimal (F* = 27 %).
Interest rate
Combined process
and scaling factor
0.0018 per week
33,333 EUR
 Data to calculate the case could be gathered easily.
 Optimization model could be adapted to the setting
of the case at hand!
10 • M. Röglinger and P. Afflerbach • An optimization model for valuating process flexibility
© FIM Research Center

11. Discussion and Outlook
Challenge 1: Provide more general guidance for decisions on process flexibility!

 Optimal level of flexibility can be calculated.
 The model can be solved analytically.
 Data can be collected easily.

 Focus on a distinct variant of flexibility.
 The model should be applied to processes
from other domains (e.g., services).
Challenge 2: Focus on positive economic effects of process flexibility!

 Risky demand
 Multi-period planning horizon
 Increased inflows from superior outputs,
decreased inflows from inferior outputs.

 Cash outflows were estimated in a rather
straightforward manner.
 It has to be critically assessed which of the
simplifying assumptions can be relaxed.
Challenge 3: Explore the benefits of treating flexibility as a multi-process concept!

 Process flexibility refers to two processes.
 One process with a superior output
 One process with an inferior output

 The providing process might benefit from
process flexibility as well.
 Process flexibility could be extended to
more than two processes.
11 • M. Röglinger and P. Afflerbach • An optimization model for valuating process flexibility
© FIM Research Center

12. ???
12 • M. Röglinger and P. Afflerbach • An optimization model for valuating process flexibility
© FIM Research Center